R: R و ggplot2 للمبتدئين: دليل شامل لقواعد الرسوم البيانية

plot(1:4, sales, type = "b")

library(ggplot2)

ggplot(sales_df, aes(x = quarter, y = sales, color = city, group = city)) +
  geom_line(linewidth = 1) +
  geom_point(size = 3) +
  labs(title = "Q1-Q4 Sales Trends", x = "Quarter", y = "Sales") +
  theme_minimal()

library(ggplot2)
ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point()

library(ggplot2)

# 1. Prepare data
df <- data.frame(
  x = 1:10,
  y = (1:10) ^ 2
)

# 2. Initialization(Data)
p <- ggplot(df, aes(x = x, y = y))

# 3. Add a Layer
p <- p + geom_point()

# 4. Add a tag
p <- p + labs(title = "Quadratic Curve", x = "X", y = "Y")

# 5. Add a topic
p <- p + theme_minimal()

# 6. Print
print(p)

ggplot(df, aes(x = x, y = y)) +
  geom_point() +
  labs(title = "Quadratic Curve", x = "X", y = "Y") +
  theme_minimal()

# Save as PNG
ggsave("myplot.png", width = 8, height = 6, dpi = 300)

# Save as PDF(For a thesis)
ggsave("myplot.pdf", width = 8, height = 6)

# Custom Size
ggsave("myplot.png", width = 10, height = 8, units = "cm", dpi = 300)

# Color + Size + Shape 3 dimensions
ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl), size = hp)) +
  geom_point(alpha = 0.7) +
  labs(title = "Vehicle Weight vs Fuel Consumption", color = "Number of cylinders", size = "Horsepower")

# aes() Inside:Map to a variable(Each dot is a different color)
ggplot(df, aes(x, y, color = group)) + geom_point()
# → Different group Display different colors

# aes() Outside:Fixed value(All dots are the same color)
ggplot(df, aes(x, y)) + geom_point(color = "red")
# → All the dots are red

ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl))) +
  geom_point(size = 3) +
  labs(title = "Vehicle Weight vs Fuel Consumption", x = "Weight", y = "Fuel Consumption")

df <- data.frame(
  x = 1:10,
  y = c(2, 4, 3, 5, 4, 6, 5, 7, 6, 8),
  group = "A"
)

ggplot(df, aes(x = x, y = y)) +
  geom_line(color = "blue", linewidth = 1) +
  geom_point(color = "blue", size = 3)

# Data
df <- data.frame(
  city = c("Beijing", "Shanghai", "Guangzhou", "Shenzhen"),
  sales = c(4800, 7000, 3900, 4500)
)

# Bar Chart
ggplot(df, aes(x = city, y = sales, fill = city)) +
  geom_bar(stat = "identity") +  # identity = Use y values
  labs(title = "Total Sales by City", x = "City", y = "Sales") +
  theme_minimal()

# 1000 A random number from a normal distribution
data <- data.frame(x = rnorm(1000, mean = 100, sd = 15))

ggplot(data, aes(x = x)) +
  geom_histogram(bins = 30, fill = "skyblue", color = "white") +
  labs(title = "Normal Distribution", x = "Value", y = "Frequency")

# 4 Class Test Scores
scores <- data.frame(
  class = rep(c("1Cls", "2Cls", "3Cls", "4Cls"), each = 10),
  score = c(rnorm(10, 80, 8), rnorm(10, 75, 10),
            rnorm(10, 85, 7), rnorm(10, 70, 12))
)

ggplot(scores, aes(x = class, y = score, fill = class)) +
  geom_boxplot() +
  labs(title = "4 Class Grade Distribution", x = "Class", y = "Fractions")

ggplot(df, aes(x, y)) +
  geom_point() +
  labs(
    title = "Main Title",
    subtitle = "Subtitle",
    caption = "Data Sources:xxx",
    x = "X Axis",
    y = "Y Axis",
    color = "Color Mapping",
    fill = "Fill Mapping"
  )

# Comparison 4 Topics
p <- ggplot(mtcars, aes(wt, mpg)) + geom_point()
(p + theme_minimal())  # Default Recommendation
(p + theme_bw())
(p + theme_classic())
(p + theme_void())

p + theme(
  plot.title = element_text(size = 16, face = "bold"),
  axis.text = element_text(size = 10),
  legend.position = "bottom"  # Legend Location
)

# 1. Built-in Color Palette
p + scale_color_brewer(palette = "Set1")    # Classic
p + scale_color_brewer(palette = "Dark2")    # Dark
p + scale_color_brewer(palette = "Pastel1")  # Light-colored

# 2. Specify manually
p + scale_color_manual(values = c("red", "blue", "green"))

# 3. rainbow
p + scale_color_manual(values = rainbow(4))

# Gradient(For continuous variables)
p + scale_color_gradient(low = "blue", high = "red")
p + scale_color_viridis_c()   # viridis(Recommendations)
p + scale_color_distiller(palette = "RdYlBu")

# ============================================
# 4 City Sales 5 Image Gallery
# Features:Use ggplot2 to create 5 publication-quality charts
# ============================================

library(ggplot2)
library(dplyr)
library(tidyr)

# 1. Prepare data
sales_wide <- tibble(
  city = c("Beijing", "Shanghai", "Guangzhou", "Shenzhen"),
  Q1 = c(1000, 1500, 800, 1200),
  Q2 = c(1200, 1800, 900, 1400),
  Q3 = c(1100, 1700, 1000, 1300),
  Q4 = c(1500, 2000, 1200, 1600)
)

sales_long <- sales_wide |>
  pivot_longer(cols = -city, names_to = "quarter", values_to = "sales")

cat("=== Sales Data ===\n")
print(sales_long)

# 2. Plot 1:Linear Trend(By City)
p1 <- ggplot(sales_long, aes(x = quarter, y = sales, color = city, group = city)) +
  geom_line(linewidth = 1) +
  geom_point(size = 3) +
  labs(title = "Q1-Q4 Sales Trends", x = "Quarter", y = "Sales", color = "City") +
  scale_color_brewer(palette = "Set1") +
  theme_minimal()

print(p1)

# 3. Plot 2:Total Sales Bar Chart
total_sales <- sales_wide |>
  mutate(total = Q1 + Q2 + Q3 + Q4) |>
  select(city, total)

p2 <- ggplot(total_sales, aes(x = reorder(city, total), y = total, fill = city)) +
  geom_bar(stat = "identity") +
  coord_flip() +  # Sideways
  labs(title = "Total Sales by City", x = "City", y = "Total Sales") +
  theme_minimal() +
  theme(legend.position = "none")

print(p2)

# 4. Plot 3:Pie Chart of Percentage Distributions(geom_bar + coord_polar)
p3 <- ggplot(total_sales, aes(x = "", y = total, fill = city)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar(theta = "y") +  # Polar Coordinates(Pie Chart)
  labs(title = "Share of Sales") +
  theme_void() +
  scale_fill_brewer(palette = "Set2")

print(p3)

# 5. Plot 4:Histogram(All Sales Data)
p4 <- ggplot(sales_long, aes(x = sales, fill = city)) +
  geom_histogram(bins = 10, alpha = 0.7, position = "identity") +
  labs(title = "Sales Distribution", x = "Sales", y = "Frequency") +
  scale_fill_brewer(palette = "Set1") +
  theme_minimal()

print(p4)

# 6. Plot 5:Box-and-Whisker Plot(By City)
p5 <- ggplot(sales_long, aes(x = city, y = sales, fill = city)) +
  geom_boxplot() +
  labs(title = "Sales Distribution by City", x = "City", y = "Sales") +
  theme_minimal() +
  theme(legend.position = "none")

print(p5)

# 7. Save All Images
ggsave("01_trend.png", p1, width = 8, height = 6, dpi = 300)
ggsave("02_total.png", p2, width = 8, height = 6, dpi = 300)
ggsave("03_pie.png", p3, width = 8, height = 6, dpi = 300)
ggsave("04_hist.png", p4, width = 8, height = 6, dpi = 300)
ggsave("05_box.png", p5, width = 8, height = 6, dpi = 300)

cat("\n=== 5 The image has been saved ===\n")
cat("  01_trend.png - Linear Trend\n")
cat("  02_total.png - Bar Chart Comparison\n")
cat("  03_pie.png - Pie Chart of Percentage Distributions\n")
cat("  04_hist.png - Histogram\n")
cat("  05_box.png - Urban Box Lines\n")

ggplot(df, aes(x, y)) + geom_point() +
  geom_text(aes(label = name), vjust = -0.5)  # Add labels above the data points